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Machine Learning Engineer

Amiri Recruiting
Full-time
On-site
Mountain View, California, United States

Machine Learning Engineer

This is an opportunity with an early stage startup.(M-F, in Mountain View, CA)

 

About the Role

We're looking for an ML research-focused software engineer to join us on our mission to build AI superpowers for developers.

 

What you'll do

  • Train and fine-tune large language models

  • Navigate high levels of uncertainty and prioritize high-value ML experiments to maximize product impact

  • Demonstrate initiative and the ability to start and make progress on projects independently

  • Swiftly design, track, and analyze experiments results. Meticulously document findings, conduct ablation studies, and synthesize data into actionable insights.

  • Participate in the ML reading group and level up the team's knowledge of LLM training and infrastructure

About you

  • Strong software engineering skills. There are no pure research scientists at the company.

  • Strong grasp of the feasibility frontier of CS, AI, and LLMs, from H100 bandwidth to GPT-4 capabilities to vector database performance.

  • Deep curiosity about the code generation problem. Willingness to constantly re-examine priors in the face of new discoveries.

  • Skilled in transforming successful experimental outcomes into robust, scalable features for the core product offering

  • Experience training and iterating on large production neural networks in any domain (self-driving, language models, etc.) is a strong plus

  • Familiarity with AI-powered developer tools like Copilot, ChatGPT, and others is a strong plus

What we believe

  • Our best work is done in person. The team goes in 5 days a week into our office in downtown Mountain View, CA (within walking distance of the Caltrain station).

  • Research is in service of a better product. While we read many papers, we won't have time to write them. The best AI researchers have excellent software engineering skills and know that infrastructure and evaluation work are critical.